Everything you need to solve the most complex analytical problems – in a single, integrated, collaborative solution.

Supports the end-to-end data mining and machine learning process with a comprehensive visual – and programming – interface. Empowers analytics team members of all skill levels with a simple, powerful and automated way to handle all tasks in the analytics life cycle.

Lets data engineers quickly build and run transformations, augment data and join data within the integrated visual pipeline of activities using a drag-and-drop interface. Performs all actions in memory to maintain data structure consistency.

Highly scalable in-memory analytical processing

Enables concurrent access to data in memory in a secure, multiuser environment. Distributes data and analytical workload operations across nodes – in parallel – multithreaded on each node for very fast speeds.

Lets modelers and data scientists access SAS capabilities from their preferred coding environment – Python, R, Java or Lua – and add the power of SAS to other applications with SAS Viya REST APIs.

From data management to model development and deployment, everyone works in the same integrated environment.

Easily solve complex analytical problems with automated insights.

SAS Visual Data Mining and Machine Learning automatically generates insights that enable you to identify the most common variables across all models, the most important variables selected across models, and assessment results for all models. Natural language generation capabilities are used to create a project summary written in simple language, enabling you to easily interpret reports. Analytics team members can add project notes to the insights report to facilitate communication and collaboration among team members.

Empower users with language options.

Don't know SAS code? No problem. SAS Visual Data Mining and Machine Learning lets you embed open source code within an analysis, and call open source algorithms seamlessly within a Model Studio flow. This facilitates collaboration across your organization, because users can program in the language of their choice. You can also take advantage of SAS Deep Learning with Python (DLPy), our open source package on GitHub, to use Python within Jupyter notebooks to access high-level APIs for deep learning functionalities, including computer vision, natural language processing, forecasting and speech processing. DLPy supports the Open Neural Network Exchange (ONNX) for easily moving models between frameworks.

Explore multiple approaches quickly to find the optimal solution.

Superior performance from massive parallel processing and the feature-rich building blocks for machine learning pipelines let you explore and compare multiple approaches rapidly. You can quickly and easily find the optimal parameter settings for diverse machine learning algorithms – including decision trees, random forests, gradient boosting, neural networks, support vector machines and factorization machines – simply by selecting the option you want. Complex local search optimization routines work hard in the background to efficiently and effectively tune your models. The solution also lets you combine unstructured and structured data in integrated machine learning programs for more valuable insights from new data types. And reproducibility in every stage of the analytics life cycle delivers answers and insights you can trust.

Boost the productivity of your analytical teams.

Data scientists, business analysts and other analytics professionals get highly accurate results from a single, collaborative environment that supports the entire machine learning pipeline. The solution enables a variety of users to access and prepare data. Perform exploratory analysis. Build and compare machine learning models. Create score code for implementing predictive models. Execute one-click model deployment. And you can do all this faster than ever before.

Reduce latency between data and decisions.

To enhance collaborative understanding, the solution provides all users with business-friendly annotations within each node describing what methods are being run, as well as information about the methods, results and interpretation. Standard interpretability reports are also available in all modeling nodes, including LIME, ICE, SHAP, PD plots, etc., with explanations in simple language from embedded natural language generation.